The digital cartography market is booming, projected to reach $45 billion by 2033, driven by autonomous vehicles, e-commerce, and GIS advancements. Explore market trends, key players (Google, TomTom, etc.), and regional analysis in this comprehensive report.

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

The emergence of deepfake geographies and the growing role that maps play in shaping public opinion on key issues has prompted cartographers to interrogate the concept of map trust. However, this growing area of research is hampered by inconsistent and untested measures of map trust. This study addresses this critical gap by developing and validating a numerical rating scale that exclusively measures map trust. A model of map trust consisting of specific indicators is derived from an exploratory factor analysis. This model is then evaluated using a confirmatory factor analysis. The results indicate that map trust can be explained from a single factor related to veracity and reliability. Two factors pertaining to bias and appearance did not explain enough variance in the model. Findings also suggest that map trust can be measured by having participants evaluate maps according to twelve empirically-derived indicators: accurate, correct, error-free, honest, trustworthy, credible, fair, reliable, reputable, objective, authentic, and balanced. Measurement validity and reliability assessments of this new scale are not only based on theory but are also empirically validated. This scale can be a useful tool for researchers and practitioners alike to measure an individual’s trust in maps.

This 1m Digital Terrain Model (DTM) is derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Polygon layer representing United States counties with name attributes.About Natural EarthNatural Earth is a convenient resource for creating custom maps. Unlike other map data intended for analysis or detailed government mapping, it is designed to meet the needs of cartographers and designers to make generalized maps. Maximum flexibility is a goal.Natural Earth is a public domain collection of map datasets available at 1:10 million (larger scale/more detailed), 1:50 million (medium scale/moderate detail), and 1:110 million (small scale/coarse detail) scales. It features tightly integrated vector and raster data to create a variety of visually pleasing, well-crafted maps with cartography or GIS software. Natural Earth data is made possible by many volunteers and supported by the North American Cartographic Information Society (NACIS).Convenience – Natural Earth solves a problem: finding suitable data for making small-scale maps. In a time when the web is awash in geospatial data, cartographers are forced to waste time sifting through confusing tangles of poorly attributed data to make clean, legible maps. Because your time is valuable, Natural Earth data comes ready to use.Neatness Counts–The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up, so you will find that all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident.GIS Attributes – Natural Earth, however, is more than just a collection of pretty lines. The data attributes are equally important for mapmaking. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers. Intelligent dataThe attributes assigned to Natural Earth vectors make for efficient mapmaking. Most lines and areas contain embedded feature names, which are ranked by relative importance. Up to eight rankings per data theme allow easy custom map “mashups” to emphasize your subject while de-emphasizing reference features. Other attributes focus on map design. For example, width attributes assigned to rivers allow you to create tapered drainages. Assigning different colors to contiguous country polygons is another task made easier thanks to data attribution.Other key featuresVector features include name attributes and bounding box extents. Know that the Rocky Mountains are larger than the Ozarks.Large polygons are split for more efficient data handling—such as bathymetric layers.Projection-friendly vectors precisely match at 180 degrees longitude. Lines contain enough data points for smooth bending in conic projections, but not so many that computer processing speed suffers.Raster data includes grayscale-shaded relief and cross-blended hypsometric tints derived from the latest NASA SRTM Plus elevation data and tailored to register with Natural Earth Vector.Optimized for use in web mapping applications, with built-in scale attributes to assist features to be shown at different zoom levels.

According to Cognitive Market Research, the global electronic cartography market size is USD 26.94 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 9.49% from 2024 to 2031. Market Dynamics of Electronic Cartography Market

Key Drivers for Electronic Cartography Market

Rising use of Smartphones and IoT - The prominent factor that drives the market growth include the widespread use of smartphones, tablets, and electronic devices. In addition rise in the usage of Internet of Things (IoT) devices, heightened the demand for real-time mapping solutions, consequently driving the demand for the electronic cartography market. In addition, growing dependence on location-based services (LBS), Geographic Information Systems (GIS), and GPS applications for searching nearby theatre halls, gasoline stations, restaurants, urban planning, disaster management, is another factor that drives the demand for electronic cartography during the forecast period.
The increasing need for real-time data mapping to create precise and current digital representations, combined with the capability to analyze and visualize streaming data from sensors, devices, and social media feeds, is expected to propel market growth.

Key Restraints for Electronic Cartography Market

Integrating geographic,and geo-social data from different sources, such as social media and satellite imagery, can be challenging due to differences in data formats and scales.
Lack of expertise among users regarding the adoption of electronic cartography in marine industry may hampered the market growth

Introduction of the Electronic Cartography Market

Electronic cartography is a technology that allows to simulate the surrounding area with the help of special technical means and computer programs. Electronic cartography integrated with various processes such as data processing, data acquisitions, map distribution, and map creation. As the demand for topographical information systems grows, the deployment of digital mapping has grown in the government and public sectors. The Science & Technology Directorate (S&T), in May 2024,has launched a digital indoor map navigator Mappedin. This digital indoor map navigator transform floor plans into interactive and easily maintainable digitized maps, and is currently being used by both response agencies and corporate clients. Mappedin provides high-quality 3D map creation, easy-to-use mapping tools and data, map sharing, and data maintenance, to city executives, building owner operators and first responders to make and deliver maps for a variety of safety-related situations—from advance preparation and planning to assistance during emergency incidents. Additionally the rapid rise in the number of smartphone and internet users has fueled industry expansion. Additionally, the increasing number of connected and semi-autonomous vehicles along with anticipated advancements in self-driving and navigation technologies, are expected to boost the demand for electronic cartography market.

These data were created for mapping usage, areas have been trimmed to the high water mark of Great Salt Lake and Utah Lake. Creates a clearer visual picture of the municipalities.

Current thru June 30, 2017

1 =  Tree cover; 2 =  Shrub cover; 3 =  Herbaceous vegetation/Grassland; 4 =  Cultivated and managed; 5 =  Mosaic of cultivated and managed/natural vegetation.

This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. Esri created this vector tile basemap from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. This version of the map is rendered using OSM cartography. The OSM Daylight map will be updated every month with the latest version of OSM Daylight data.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site:www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this enhanced vector basemap available to the ArcGIS user and developer communities.

This 1m Digital Surface Model (DSM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Discover the latest trends in the $17.86 billion Electronic Cartography market. Explore its steady growth, key players like Garmin & Navionics, and the impact of advanced technologies like GPS & GIS. Learn about market drivers, restraints, and future projections for 2025-2033.

Cartography Software Market Outlook

According to our latest research, the global cartography software market size reached USD 2.15 billion in 2024, driven by increasing demand for advanced mapping solutions across diverse sectors. The market is expected to expand at a CAGR of 9.2% between 2025 and 2033, with the market size forecasted to reach USD 4.79 billion by 2033. This robust growth is primarily attributed to rapid urbanization, the proliferation of geospatial data, and growing integration of GIS technologies in government and commercial applications.

The primary growth factor propelling the cartography software market is the accelerating adoption of geospatial intelligence and geographic information systems (GIS) across various sectors. Governments, urban planners, and commercial enterprises are increasingly leveraging cartography software for enhanced decision-making, spatial data visualization, and resource management. The surge in smart city initiatives and infrastructure development projects worldwide is further boosting demand for sophisticated mapping tools. These tools enable stakeholders to visualize complex datasets, analyze spatial relationships, and optimize planning processes, thereby improving efficiency and reducing operational costs.

Another significant driver is the technological evolution within the cartography software landscape. The integration of artificial intelligence, machine learning, and cloud computing has transformed traditional mapping solutions into dynamic, interactive, and real-time platforms. These advancements have broadened the application scope of cartography software, making it indispensable in fields such as disaster management, environmental monitoring, and business intelligence. The ability to process large volumes of geospatial data quickly and accurately has enhanced the value proposition of cartography solutions, attracting investments from both public and private sectors.

Furthermore, the growing need for disaster risk management and environmental monitoring is catalyzing the adoption of cartography software. Governments and humanitarian organizations are increasingly utilizing these tools to map vulnerable areas, monitor climate change impacts, and plan emergency response strategies. The software’s capability to provide real-time situational awareness and predictive analytics is critical in mitigating risks and enhancing preparedness. As climate-related challenges intensify, the reliance on advanced cartographic solutions is expected to deepen, further fueling market growth.

From a regional perspective, North America currently dominates the cartography software market, supported by substantial investments in geospatial infrastructure and a high concentration of technology-driven enterprises. However, Asia Pacific is poised for the fastest growth, driven by rapid urbanization, expanding infrastructure projects, and increasing government focus on smart city development. Europe also holds a significant share, benefiting from robust regulatory frameworks and widespread adoption of GIS technologies across various sectors. The Middle East & Africa and Latin America are emerging as promising markets, with growing awareness of the benefits of digital mapping in resource management and urban planning.

Component Analysis

The cartography software market by component is bifurcated into software and services. The software segment captures the largest market share, accounting for over 65% in 2024, owing to the widespread adoption of advanced mapping solutions across government, commercial, and utility sectors. Modern cartography software platforms offer comprehensive features such as data visualization, spatial analysis, and real-time collaboration, making them indispensable tools for urban planners, environmental agencies, and businesses. The proliferation of open-source platforms and the availability of customizable mapping solutions have further accelerated the adoption of cartography software globally.
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This dataset contains three shapefiles with buildings, derived from three different VHR satellite images of 2005, 2010, and 2014 of the city of Goma in the eastern DR Congo. These layers have been generated by manually digitizing each individual building, based on visual interpretation of the imagery. These building layers serve as the base for quantitative analysis and mapping of urban development - and hence constitute the geodata the article is based on.

Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Geofabric Surface Cartography product provides a set of related feature classes to be used as the basis for the production of consistent hydrological cartographic maps. This product contains a geometric representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs, canals and other hydrographic features).

The product is fully topologically correct which means that all the stream segments flow in the correct direction.

This product contains fifteen feature types including: Waterbody, Mapped Stream, Mapped Node, Mapped Connectivity (Upstream), Mapped Connectivity (Downstream), Sea, Estuary, Dam, Structure, Canal Line, Water Pipeline, Terrain Break Line, Hydro Point, Hydro Line and Hydro Area.

Purpose

This product contains a geometric representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for the production of consistent hydrological cartographic map products, as well as the visualisation of surface hydrology within a GIS to support the selection of features for inclusion in cartographic map production.

This product can also be used for stream tracing operations both upstream and downstream however, as this is a mapped representation, streams may be represented as interrupted or intermittent features. In contrast, the Geofabric Surface Network product represents the same stream as a continuous connected feature, that is, the path that stream would take (according to the terrain model) if sufficient water were available for flow. Therefore, for stream tracing operations where full stream connectivity is required, the Geofabric Surface Network product should be used.

Dataset History

Geofabric Surface Cartography is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The source data input for the Geofabric Surface Cartography product is the AusHydro v1.7.2 (AusHydro) surface hydrology data set. The AusHydro database provides a seamless surface hydrology layer for Australia at a nominal scale of 1:250,000. It consists of lines, points and polygons representing natural and man-made features such as watercourses, lakes, dams and other water bodies. The natural watercourse layer consists of a linear network with a consistent topology of links and nodes that provide directional flow paths through the network for hydrological analysis.

This network was used to produce the GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3 of Australia (https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=66006).

Geofabric Surface Cartography is an amalgamation of two primary datasets. The first is the hydrographic component of the GEODATA TOPO 250K Series 3 (GEODATA 3) product released by Geoscience Australia (GA) in 2006. The GEODATA 3 dataset contains the following hydrographic features: canal lines, locks, rapid lines, spillways, waterfall points, bores, canal areas, flats, lakes, pondage areas, rapid areas, reservoirs, springs, watercourse areas, waterholes, water points, marine hazard areas, marine hazard points and foreshore flats.

It also provides information on naming, hierarchy and perenniality. The dataset also contains cultural and transport features that may intersect with hydrographic features. These include: railway tunnels, rail crossings, railway bridges, road tunnels, road bridges, road crossings, water pipelines.

Refer to the GEODATA 3 User Guide http://www.ga.gov.au/meta/ANZCW0703008969.html for additional information.

Dataset Citation

Bureau of Meteorology (2011) Geofabric Surface Cartography - V2.1. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/5342c4ba-f094-4ac5-a65d-071ff5c642bc.

MLU is a geographic database focused on urban land use for the Community of Madrid, including all the municipalities of the Community of Madrid.

This data contains the dataset for the Master’s thesis “Use Cases and Limitations of Webcam Eye Tracking for Cartography Research” by Rahimeh Gharibpour, under supervisor Dr. Merve Keskin, submitted to the University of Salzburg, UNIGIS program. Full Thesis PDF. The thesis explores webcam-based eye tracking as a low-cost, scalable alternative to traditional lab-based systems for studying map cognition in cartographic research. The experiment replicated a subset of tasks from the CartoGAZE (2023) study, available at here, focusing on spatial memory and recognition of road and hydrographic features on 2D static Google road maps. A total of 30 map stimuli were used, divided into three blocks: * Block 1: 10 map stimuli focusing on the memorability of main roads and road junctions. * Block 2: 10 map stimuli focusing on the memorability of major water bodies and rivers. * Block 3: 10 map stimuli combining elements from the first two blocks to assess their collective memorability. The study involved 35 participants in an online experiment, accessible via here, with data from 28 participants analyzed after applying a 70% calibration accuracy threshold. Cognitive load was assessed using both behavioral metrics (response times, success rates) and eye-tracking metrics (average fixation duration, fixations per second, average saccade length). Results suggest that webcam-based eye tracking can replicate general attentional patterns observed in lab-based studies, but with reduced precision due to lower sampling rates (15–20 Hz vs. 250 Hz), environmental variability, and technical factors such as device differences and participant movement. See the GitHub repository for the source code dataset: https://github.com/rahgh/WebcamET_CartoGAZE-data-set

This dataset contains useful vector maps to work with with Goode's Homolosine projection. The list of files included are:

• CounterDomain.geojson - a polygonal approximation of the Homolosine projection counter-domain. This can be used to fix vectors wrongly projected by programmes that consider the counter-domain to be infinite. It can also be used to represent the seas in global mapping.
• ParallelsMeridians.geojson - a set of meridians and parallels to be used in the creation of global maps.
• Homolosine.crs - the PROJ string defining the Homolosine projection (referenced by the GeoJSON slides)
• LICENCE - full text of the licence (EUPL-1.2)

These datasets were generated with the open souce programme homolosine-vectors, available at: https://gitlab.com/ldesousa/homolosine-vectors

The electronic cartography market is set to witness gradual growth between 2025 and 2035, fueled by the growing demand for sophisticated navigation systems, GIS-based analytics, and AI-based mapping solutions. The industry is expected to reach USD 32.26 billion in 2025 and expand to USD 48.74 billion by 2035, reflecting a compound annual growth rate (CAGR) of 10% during the forecast period.

Contract & Deals Analysis - Electronic Cartography Market

Company	Contract Value (USD Mn)
Garmin Ltd.	Approximately USD 90 - USD 100
Navionics (A Garmin Company)	Approximately USD 80 - USD 90
C-MAP (Navico Group)	Approximately USD 70 - USD 80
Maxar Technologies	Approximately USD 100 - USD 110

Country-wise Outlook

Country	CAGR (2025 to 2035)
USA	10.2%
China	10.9%
Germany	9.7%
Japan	10.1%
India	11.4%
Australia	9.9%

Competition Outlook

Company Name	Estimated Market Share (%)
Garmin Ltd.	20-25%
Navionics (Garmin)	15-20%
Maxar Technologies	10-15%
TomTom NV	8-12%
Honeywell Aerospace	5-10%
Thales Group	4-8%
Other Companies (combined)	30-38%

View Map in ArcGIS Important: Due to the size of this dataset, this item may be slower to display. Vector tile services have been shared to the Digital Atlas of Australia to support faster national scale visualisation of larger sublayers, including Mapped Streams, Canal Lines, Water Bodies and Dams. A file geodatabase of this product is also available to download at the following link Download: Geofabric Surface Hydrology CartographyAbstractThis dataset is part of the Australian Hydrological Geospatial Fabric (AHGF) also known as the Geofabric. The Geofabric Surface Hydrology Cartography version 3.3 product provides a set of related feature classes used to create consistent hydrological maps. It includes a geometric representation of major surface water features across Australia, excluding external territories. Most features are natural, such as rivers and lakes, but the product also includes some man-made elements like reservoirs, canals and other hydrographic structures.The dataset is fully topologically correct, meaning all stream segments flow in the correct direction. It includes fifteen feature types: Waterbody, Mapped Stream, Mapped Node, Mapped Connectivity (Upstream), Mapped Connectivity (Downstream), Sea, Estuary, Dam, Structure, Canal Line, Water Pipeline, Terrain Break Line, Hydro Point, Hydro Line and Hydro Area.Product Guide: ahgf_productguide_V3_0_release.pdfProduct Schema: Visio-AHGF_GDB_SHCartography_Schema_V2_1_release.vsdData Dictionary: ahgf_data_dictionary_surface_cartography_V2_1_release.pdfData Product Specifications: ahgf_dps_surface_cartography_V2_1_release.pdfCurrencyDate modified: 2022Modification frequency: As neededData extentSpatial extentNorth: -8.9°South: -44.0°East: 154.1°West: 112.8°Source informationDownloaded from the geofabric download page on the 17th of April 2025: Downloads: Geofabric: Water Information: Bureau of MeteorologyLineage statementGeofabric Surface Cartography is part of a suite of products developed by the Australian Bureau of Meteorology. The source data for this product comes mainly from AusHydro V2, which includes Geoscience Australia's national surface hydrology database. This database is based on selected features from regional surface hydrology datasets, mapped at a scale of 1:250,000. Additional regional features have been added to ensure important elements, such as flow paths, are included. Monitoring points from the Bureau’s Australian Water Resources Information System (AWRIS) are also part of the dataset.The product includes lines, points and polygons that represent both natural and man-made features, such as rivers, canals, lakes, dams, water pipelines and monitoring points. The natural watercourse layer forms a linear network with consistent topology, meaning stream segments are connected and flow in the correct direction. This network, along with key waterbodies, supports the creation of other Geofabric products, including Geofabric V3 SH_Network and SH_Catchment, as well as associated V3 1-second DEM-H and D8 grids.Each feature in the AusHydro V2 dataset has a unique identifier called AusHydro-ID. This identifier helps maintain the dataset and allows future integration of higher-resolution data. It also links to ANUDEM Derived Streams through a shared segment ID and ultimately connects to the National Catchments Boundaries (NCBs).To create this dataset, the AusHydro Surface Hydrology data is first loaded into the Geofabric development GIS environment. Feature classes are then restructured into composite hydrography datasets within the Geofabric Maintenance Geodatabase. These are assigned unique Hydro-IDs using Esri ArcHydro for Surface Water (version 1.4.0.180 and ApFramework 3.1.0.84). Finally, the updated feature classes are transferred to the Geofabric Surface Cartography Feature Dataset within its dedicated geodatabase.The Digital Atlas of Australia team at Geoscience Australia has made minor geometry repairs to support online publication. They also updated cartographic elements such as visibility, labels and symbology to improve accessibility and performance.ContactBureau of Meteorology, Community Services Group, ahgf@bom.gov.au